mindspore.nn.MultilabelMarginLoss

class mindspore.nn.MultilabelMarginLoss(reduction='mean')

Creates a loss criterion that minimizes the hinge loss for multi-class classification tasks. It takes a 2D mini-batch Tensor $x$ as input and a 2D Tensor $y$ containing target class indices as output.

Each sample in the mini-batch, the loss is computed as follows:

$$\text{loss}(x,y)=\sum _{ij}\frac{max(0,1-(x[y[j]]-x[i]))}{\text{x.size}(0)}$$

where $x\in \{0,\cdots ,\text{x.size}(0)-1\}$, $y\in \{0,\cdots ,\text{y.size}(0)-1\}$, $0\le y[j]\le \text{x.size}(0)-1$, and for all $i$ and $j$, $i$ does not equal to $y[j]$.

Furthermore, both $y$ and $x$ should have identical sizes.

Note

For this operator, only a contiguous sequence of non-negative targets that starts at the beginning is taken into consideration, which means that different samples can have different number of target classes.

Parameters

reduction (str, optional) –

Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'mean' .

• 'none': no reduction will be applied.

• 'mean': compute and return the mean of elements in the output.

• 'sum': the output elements will be summed.

Inputs:

• x (Tensor) - Predict data. Tensor of shape $(C)$ or $(N,C)$, where $N$ is the batch size and $C$ is the number of classes. Data type must be float16 or float32.

• target (Tensor) - Ground truth data, with the same shape as x, data type must be int32 and label targets padded by -1.

Outputs:

• y (Union[Tensor, Scalar]) - The loss of MultilabelMarginLoss. If reduction is "none", its shape is $(N)$. Otherwise, a scalar value will be returned.

Raises

• TypeError – If x or target is not a Tensor.

• TypeError – If dtype of x is neither float16 nor float32.

• TypeError – If dtype of target is not int32.

• ValueError – If length of shape of x is neither 1 nor 2.

• ValueError – If shape of x is not the same as target.

• ValueError – If reduction is not one of 'none', 'mean', 'sum'.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> import numpy as np
>>> loss = nn.MultilabelMarginLoss()
>>> x = ms.Tensor(np.array([[0.1, 0.2, 0.4, 0.8], [0.2, 0.3, 0.5, 0.7]]), ms.float32)
>>> target = ms.Tensor(np.array([[1, 2, 0, 3], [2, 3, -1, 1]]), ms.int32)
>>> output = loss(x, target)
>>> print(output)
0.325